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42 Cards in this Set

  • Front
  • Back
random phenomenon
individual outcomes are uncertain but there is a regular distribution of outcomes in a large number of repetitions
probability
proportion of times the outcome would occur in a long series of repetitions
independent trials
the outcome of one trial must not influence the outcome of any other
Sample space S (random phenomenon)
The set of all possible outcomes
Event
an outcome or a set of outcomes of a random phenomenon (a subset of the sample space)
4 Probability rules
1. 0 ≤ P(A) ≤ 1
2. P(S) = 1
3. Disjoint events (no outcomes in common)-P(A or B) = P(A) + P(B)
4. Complement rule P(Ac) = 1 - P(A)
Disjoint events
Events that have no outcomes in common
Probability of any event (description)
Equals the sum of the probabilities of the outcomes making up the event
Probability of any event A (equation)
P(A) = (count of outcomes in A) / (count of outcomes in S)
Multiplication rule for independent events
Two events A and B are independent if knowing that one occurs does not change the probability that the other occurs so P(A and B) = P(A)P(B)
Disjoint and independet
Disjoint events cannot be independent (they can't occur together, so the fact that A occurs already says that B cannot occur)
Random variable
A variable whose value is a numerical outcome of a random phenomenon
Discrete random variable (X)
A random variable with a finite number of possible values
Probability distribution of discrete X
Lists the values and their probabilities
Probabilities of values of X (must satisfy these two things)
1. Every probability pi is a number between 0 and 1
2. p1 + p2 + ... + pk = 1
(probability of any event comprising any of the xi values is found by adding up their respective pi values)
Probability histograms
Compare the probability of certain numerical outcomes
Continuous random variable X
Takes all values in an interval of numbers
Probability distribution of continuous X
Described by a density curve; the probability of any event is the area under the density curve and above the values of X that make up the event
Continuous probability distributions and individual outcomes
All are assigned probabilities of zero
Continuous probaility of normal distribution
Calculate probabilities using z-scores

Z = (X - µ) / σ
Mean of discrete random variable X
Σxipi = µx = x1p1 + x2p2 + ... + xnpn
Law of large numbers
Draw independent observations at random from any population with finite mean µ. Decide how accurately you would like to estimate µ. As the number of observations drawn increases, the mean x-bar of the observed values eventually approaches the mean µ of the population as closely as you specified and then stays that close.
Variability of the random outcomes
The more variable the outcomes, the more trials are needed to ensure that mean outcome x-bar close to the distribution mean µ
Linear transformation of the mean of a random variable
µ(new) = a + bµ
Addition of random variable means
µ(x+y) = µ(x) +µ(y)
Variance of a discrete random variable
Σ(xi - µx)^2(pi)

(standard dev, is sqrt)
Independence and adding variance
Variances only add when there is no association between variables
Variance of the sum of two non-independent variables
Depends on the correlation between the variables (it is non-zero whenever two random variables are not independent)
Linear transformation (a, b) for variances
σ^2 (new) = b^2σ^2
σ^2(X+Y) or σ^2(X-Y), independent equals
σ^2(X) +σ^2(Y)
Adding variances with correlation p
(X+Y)-->add 2pσ(X)σ(Y)
(X-Y)-->sub. 2pσ(X)σ(Y)
Union (of collection of events
The event that at least one of the collection occurs
P(one or more of A, B, C)
P(A) + P(B) + P(C)
P(A or B) equals
P(A) + P(B) - P(A and B)
Conditional probability
Computing probability given the fact that some event has already happened
Multiplication rule: P(A and B) equals
P(A)P(B l A)

(P(B) given A)
Conditional probability (P(A)>0)
P(B l A) = P(A and B) / P(A)
Intersection
Event that all of the events occur (out of any collection of events)
P(A and B and C)
P(A)P(B l A)P(C l A and B)
Tree diagram
(A and B)-->multiply each nodal probability along a branch
P(B l A)-->calculate probability starting from the node where A is decided
Independent events (conditional probability)
P(B l A) = P(B)
Baye's Rule
A1, A2, ..., Ak are disjoint events without P=0 and add to 1

P(Ai l C) = [P(C l Ai)P(Ai)] / [P(C l A1)P(A1) + ... + P(Ak)P(C l Ak)

C is another event whose probability is not 0 or 1